mos2128
Mari Sanders
2024-11-26
res=GET('https://developer.nps.gov/api/v1/parks?limit=500&api_key=B9nDpbkbrb3kSOjz6kXSxMJ3d6MSpUvt1QqYdeyn')
data = res %>% content("text") %>% jsonlite::fromJSON() %>% as_tibble()
NPS_data=data %>% unnest(data) %>% select(fullName,latitude,longitude,activities,states, parkCode) %>% janitor::clean_names() %>%
mutate(
latitude = as.numeric(latitude),
longitude = as.numeric(longitude)
)visitation_data <-
read_csv("data/Query Builder for Public Use Statistics (1979 - Last Calendar Year).csv") %>%
janitor::clean_names() %>%
mutate(unit_code = tolower(unit_code)) %>%
rename(park_code = unit_code,
full_name = park_name) %>%
select(full_name, park_code, park_type, region, state, year, month, recreation_visits, tent_campers, rv_campers, tent_campers, backcountry)## Rows: 101419 Columns: 35
## ── Column specification ────────────────────────────────────────────
## Delimiter: ","
## chr (10): ParkName, UnitCode, ParkType, Region, State, ParkNameTotal, UnitCo...
## dbl (3): Year, Month, YearTotal
## num (22): RecreationVisits, NonRecreationVisits, RecreationHours, NonRecreat...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.combined_data <- right_join(NPS_data, visitation_data, by = c("park_code"))faceted plot with rv, tent, and backcountry visits
visitation_data %>%
group_by(park_type, month) %>%
summarize(total_visitation = sum(recreation_visits)) %>%
plot_ly(x = ~as.factor(month), y = ~total_visitation, type = "scatter", mode = "lines", color = ~park_type)## `summarise()` has grouped output by 'park_type'. You can override
## using the `.groups` argument.## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsvisit_summary <-
visitation_data %>%
group_by(park_type, month) %>%
summarize(total_tent = sum(tent_campers),
total_backcountry = sum(backcountry),
total_rv = sum(rv_campers))## `summarise()` has grouped output by 'park_type'. You can override
## using the `.groups` argument.tent_campers_plot <- plot_ly(visit_summary,
x = ~as.factor(month),
y = ~total_tent,
type = "scatter",
mode = "lines",
color = ~park_type,
legendgroup = ~park_type) %>%
layout(title = "Tent Campers")
backcountry_plot <- plot_ly(visit_summary,
x = ~as.factor(month),
y = ~total_backcountry,
type = "scatter",
mode = "lines",
color = ~park_type,
legendgroup = ~park_type,
showlegend = FALSE) %>%
layout(title = "Backcountry")
rv_campers_plot <- plot_ly(visit_summary,
x = ~as.factor(month),
y = ~total_rv,
type = "scatter",
mode = "lines",
color = ~park_type,
legendgroup = ~park_type,
showlegend = FALSE) %>%
layout(title = "RV Campers")
subplot(
tent_campers_plot,
backcountry_plot,
rv_campers_plot,
nrows = 2,
shareX = TRUE,
shareY = TRUE
)## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsvisits by season boxplot
visitation_data %>%
mutate(season = case_when(
month %in% c(12,1,2) ~ "Winter",
month %in% c(3,4,5) ~ "Spring",
month %in% c(6,7,8) ~ "Summer",
TRUE ~ "Fall"
)) %>%
plot_ly(x = ~season, y = ~recreation_visits, type = "box")total visitation by year
visitation_data %>%
group_by(park_type, year) %>%
summarize(total_visitation = sum(recreation_visits)) %>%
ggplot(aes(x = as.factor(year), y = total_visitation, color = park_type, group = park_type)) +
geom_line(linewidth = 0.7) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))## `summarise()` has grouped output by 'park_type'. You can override
## using the `.groups` argument.
total visitation by season
visitation_data %>%
mutate(season = case_when(
month %in% c(12,1,2) ~ "Winter",
month %in% c(3,4,5) ~ "Spring",
month %in% c(6,7,8) ~ "Summer",
TRUE ~ "Fall"
)) %>%
group_by(season) %>%
summarize(total_tent = sum(tent_campers),
backcountry_visits = sum(backcountry),
total_rv = sum(rv_campers)) %>%
pivot_longer(
total_tent:total_rv,
values_to = "total_visit",
names_to = "type_visit",
names_prefix = "total_"
) %>%
plot_ly(x = ~season, y = ~total_visit, color = ~type_visit, type = "bar")mean visitation by park type and season
visitation_data %>%
mutate(season = case_when(
month %in% c(12,1,2) ~ "Winter",
month %in% c(3,4,5) ~ "Spring",
month %in% c(6,7,8) ~ "Summer",
TRUE ~ "Fall"
)) %>%
group_by(park_type, season) %>%
mutate(
mean_total = mean(recreation_visits, na.rm = TRUE)
) %>%
plot_ly(x = ~season, y = ~mean_total, color = ~park_type)## No trace type specified:
## Based on info supplied, a 'bar' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#bar## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsmean visitation season and visit type
visitation_data %>%
mutate(season = case_when(
month %in% c(12,1,2) ~ "Winter",
month %in% c(3,4,5) ~ "Spring",
month %in% c(6,7,8) ~ "Summer",
TRUE ~ "Fall"
)) %>%
group_by(season) %>%
summarize(mean_tent = mean(tent_campers),
mean_backcountry = mean(backcountry),
mean_rv = mean(rv_campers)) %>%
pivot_longer(
mean_tent:mean_rv,
names_to = "type_visit",
values_to = "mean",
names_prefix = "mean_"
) %>%
plot_ly(x = ~season, y = ~mean, color = ~type_visit)## No trace type specified:
## Based on info supplied, a 'bar' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#barmap of all national parks in US
leaflet(data = NPS_data) %>%
addTiles() %>%
addMarkers(~longitude, ~latitude) %>%
setView(lng = -98.583, lat = 39.828, zoom = 4)Mean recreation visit by park type (separate plots for each park type)
park_types <- unique(visitation_data$park_type)
plots <- list()
for (i in seq_along(park_types)) {
plots[[i]] <- visitation_data %>%
filter(park_type == park_types[i]) %>%
mutate(season = case_when(
month %in% c(12, 1, 2) ~ "Winter",
month %in% c(3, 4, 5) ~ "Spring",
month %in% c(6, 7, 8) ~ "Summer",
TRUE ~ "Fall"
)) %>%
group_by(season) %>%
summarize(avg_visits = mean(recreation_visits, na.rm = TRUE)) %>%
ggplot(aes(x = season, y = avg_visits, fill = season)) +
geom_col() +
ggtitle(paste("Average Visits by Season for", park_types[i]))
}
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National Preserves has pretty similar avg visits across seasons
Average visitation by type and park_type (separate graph for each park_type)
park_types <- unique(visitation_data$park_type)
plots <- list()
for (i in seq_along(park_types)) {
visit_summary <- visitation_data %>%
filter(park_type == park_types[i]) %>%
mutate(season = case_when(
month %in% c(12, 1, 2) ~ "Winter",
month %in% c(3, 4, 5) ~ "Spring",
month %in% c(6, 7, 8) ~ "Summer",
TRUE ~ "Fall"
)) %>%
group_by(season) %>%
summarize(
mean_tent = mean(tent_campers, na.rm = TRUE),
mean_backcountry = mean(backcountry, na.rm = TRUE),
mean_rv = mean(rv_campers, na.rm = TRUE)
) %>%
pivot_longer(
cols = starts_with("mean_"),
names_to = "type_visit",
values_to = "mean",
names_prefix = "mean_"
)
plots[[i]] <- ggplot(visit_summary, aes(x = season, y = mean)) +
geom_col() + facet_grid(~type_visit) +
ggtitle(paste("Average Visits by Season for", park_types[i]))
}
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